Data Strategy

Our platform's value is derived from the data we process. We treat data schema design, system locality, and architectural storage choices with intense scrutiny to maintain high performance, strict privacy, and operational simplicity.

Polyglot Persistence & Intentional Consolidation

We embrace Polyglot Persistence—using the precise tool designed for the job. We utilize dedicated Key-Value stores for fast caching and dedicated blob storage for unstructured assets. We do not force a single database to handle every single operational requirement.

However, we balance this with operational simplicity. Distributing core state across too many disparate databases introduces profound complexity regarding distributed transactions and failure states.

Postgres and the pgvector Nuance

We rely on PostgreSQL as our primary data store for relational business logic. While we embrace polyglot storage across the broader platform, we explicitly reject adopting standalone vector databases simply to follow a trend.

We embrace Integrated Vector Storage (via extensions like pgvector).

• Avoiding AI Silos: Keeping vector embeddings closely bound to normal structured relational data ensures immediate data consistency.
• Unified Queries: By writing embeddings directly into Postgres, engineers can execute a single transaction that performs a similarity search on the vector while simultaneously applying complex relational JOINs and WHERE clauses based on standard metadata.
• ACID Compliance: This consolidation provides strict ACID guarantees to our AI features. It absolutely eliminates the risk of a "split brain" state where our relational business logic falls out of sync with an external vector index.

CQRS & Caching

While we consolidate our primary truth into Postgres, we strictly separate our read patterns from our write patterns (CQRS) to guarantee high performance under asymmetric load.

• Asynchronous Materialization: Heavy write operations append to primary tables, while background workers asynchronously synthesize and project that data into query-optimized read structures.
• Aggressive Caching: We heavily cache our read paths. We utilize fast Key-Value stores (like Redis) at the edge to serve pre-computed API responses, ensuring our primary database is protected from read-heavy traffic spikes.

Privacy & Data Boundaries

Data privacy is non-negotiable and heavily integrated into our storage architecture down to the lowest schema levels.

• Strict Tenant Isolation: Every data entity is explicitly partitioned by Tenant ID. We utilize Row-Level Security (RLS) policies within the database kernel to guarantee that cross-tenant data leaks are nearly impossible, acting as a failsafe against application-layer API bugs.
• Data Minimization: We store only what we need. Personal data is aggressively minimized, encrypted at rest, and subjected to automated expiration policies.